# Are LLMs becoming similarly creative? Evidence from three years of models

arXiv: https://arxiv.org/abs/2608.19437 · HTML: https://mlsanity.com/paper/2608.19437/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 39 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)](https://mlsanity.com/paper/2510.22954/) (2025)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Clio: Privacy-Preserving Insights into Real-World AI Use](https://mlsanity.com/paper/2412.13678/) (2024)
- [Base Models Beat Aligned Models at Randomness and Creativity](https://mlsanity.com/paper/2505.00047/) (2025)
- [Echoes in AI: Quantifying lack of plot diversity in LLM outputs](https://mlsanity.com/paper/2501.00273/) (2024)
- [LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing](https://mlsanity.com/paper/2507.00769/) (2025)
- [Assessing and Understanding Creativity in Large Language Models](https://mlsanity.com/paper/2401.12491/) (2024)
- [MacGyver: Are Large Language Models Creative Problem Solvers?](https://mlsanity.com/paper/2311.09682/) (2023)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context](https://mlsanity.com/paper/2412.17596/) (2024)
- [Anthropic Economic Index report: Uneven geographic and enterprise AI adoption](https://mlsanity.com/paper/2511.15080/) (2025)
- [Generative Monoculture in Large Language Models](https://mlsanity.com/paper/2407.02209/) (2024)
- [Evaluating Creative Short Story Generation in Humans and Large Language Models](https://mlsanity.com/paper/2411.02316/) (2024)
- [Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models](https://mlsanity.com/paper/2510.12110/) (2025)
- [What happens when generative AI models train recursively on each others' outputs?](https://mlsanity.com/paper/2505.21677/) (2025)
- [Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models](https://mlsanity.com/paper/2601.20546/) (2026)
- [Incentives shape how humans co-create with generative AI](https://mlsanity.com/paper/2604.03529/) (2026)

## Built on by (most cited first, 2 of 2)

- [When Pair Count Is Not the Sample Size: What All-Pairs Agent Comparisons Estimate](https://mlsanity.com/paper/2609.33012/) (2026)
- [Reinforcing Agentic Creativity in Scientific Ideation with Night Science](https://mlsanity.com/paper/2609.35706/) (2026)

## Abstract (arXiv)

Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality. As LLMs increasingly support human ideation and creative work, understanding trends in LLM performance on open-ended tasks is critical. This paper presents a preliminary analysis of LLM creative outputs spanning three years of model releases, examining model responses to Infinity-Chat100, a real-world collection of open-ended user queries, and the Alternate Uses Task, an established psychometric creativity assessment. Using sentence-embedding similarity, we examine trends in LLM responses to these prompts. Our findings show a statistically significant decrease in model output diversity over time, suggesting that LLM outputs may be converging in creative substance across models. If this trend persists, LLM-driven homogenization may progressively diminish human agency in human-AI co-creative work, demanding careful consideration of LLMs' role in the human creative process.